¿Which are the trends from the OECD member countries?

R&D plays a leading role in the innovation process of any country. It’s key in gaining competitive advantages that favor education, technology, science and all those areas that generate economic and social development.

The following visualizations combine insights from the OECD’s publication, “Main Science and Technology Indicators”, with the goal of showing the state of R&D in member countries.

NOTE | OECD member countries: Australia, Austria, Belgium, Canada, Chile, Czech Republic, Denmark, Estonia, Finland, France, Germany, Greece, Hungary, Iceland, Ireland, Israel, Italy, Japan, Korea, Latvia, Luxembourg, Mexico, the Netherlands, New Zealand, Norway, Poland, Portugal, Slovak Republic, Slovenia, Spain, Sweden, Switzerland, Turkey, United Kingdom, and United States.

Loading libraries

library(readxl)                 # load data in xlsx format
library(dplyr)                  # work with dataframes

## 
## Attaching package: 'dplyr'

## The following objects are masked from 'package:stats':
## 
##     filter, lag

## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union

library(ggplot2)                # create plots
library(sf)                     # encode spatial vector data

## Linking to GEOS 3.6.1, GDAL 2.2.3, proj.4 4.9.3

library(rnaturalearthdata)      # world vector map data
library(leaflet)                # create interactive maps
library(extrafont)              # fonts

## Registering fonts with R

Importing data

GDE_on_R_D <- read_excel("data/GDE_on_R_D.xlsx")

Tidying database and creating dataframes

Dataframe 1: Percentage financed by Government and Industry

financed <- GDE_on_R_D %>%
  select(country, region, percentage_financed_by, financed_by) %>% 
  filter(financed_by %in% c("Industry", "Government"))

head(financed)

## # A tibble: 6 x 4
##   country region  percentage_financed_by financed_by
##   <chr>   <chr>                    <dbl> <chr>      
## 1 Austria Europe                    49.7 Industry   
## 2 Austria Europe                    32.6 Government 
## 3 Belgium Europe                    58.6 Industry   
## 4 Belgium Europe                    22.5 Government 
## 5 Canada  America                   41.6 Industry   
## 6 Canada  America                   32.2 Government

Dataframe 2: Percentage performed by Government, Higher education and Industry

performed <- GDE_on_R_D %>%
  select(country, region, percentage_performed_by, performed_by) %>% 
  filter(performed_by %in% c("Industry", "Higher education", "Government"))

head(performed)

## # A tibble: 6 x 4
##   country   region  percentage_performed_by performed_by    
##   <chr>     <chr>                     <dbl> <chr>           
## 1 Australia Oceania                    53.4 Industry        
## 2 Australia Oceania                    30.6 Higher education
## 3 Australia Oceania                    12.7 Government      
## 4 Austria   Europe                     71.4 Industry        
## 5 Austria   Europe                     23.5 Higher education
## 6 Austria   Europe                      4.6 Government

Dataframe 3: Total researchers in full time

researchers <- GDE_on_R_D %>%
  select(country, total_researchers_in_full_time, year) %>% 
  filter(year=="2015")

head(researchers)

## # A tibble: 6 x 3
##   country    total_researchers_in_full_time  year
##   <chr>                               <dbl> <dbl>
## 1 Austria                             43562  2015
## 2 Belgium                             53178  2015
## 3 Chile                                8175  2015
## 4 Czech Rep.                          38081  2015
## 5 Denmark                             42425  2015
## 6 Estonia                              4187  2015

Creating Bar Plots

Visualizing the percentage financed by Government and Industry

ggplot(financed, 
       aes(x=country, y=percentage_financed_by, fill=financed_by)) +
  geom_bar(stat="identity", width = .8) +
  coord_flip() +
  facet_grid(.~ financed_by) +
  labs(x="", y="", fill="Financed by",
       title = "GROSS DOMESTIC EXPENDITURE ON R&D (OECD MEMBER COUNTRIES)",
       subtitle = "Percentage financed by Government and the Business enterprise sector (2015)",
       caption = "Source: OECD, Main Science and Technology Indicators, 2018. | DataViz: Lorna Campos Matus") +
  theme_minimal() +
  geom_text(aes(label=percentage_financed_by), color="black", family="Arial Rounded MT Bold", size=3, vjust=.38, hjust=-.3)+
  theme(strip.background = element_blank(),
        strip.text.x = element_blank(), 
        axis.text.x = element_blank(),
        axis.text=element_text(size=10, color = "black"),
        title=element_text(color="black",size=10, family="A Love of Thunder")) +
  scale_fill_manual(values=c("#c7093f", "#008b8e")) + 
  scale_color_manual(values=c("#c7093f"="Government", "#008b8e"="Industry"))

knitr::include_graphics("data/Barplot - Financed.png")

No data recorded for Australia

Visualizing the percentage performed by Government, Higher education and Industry

ggplot(performed, 
       aes(x=country, y=percentage_performed_by, fill=performed_by)) +
  geom_bar(stat="identity") +
  coord_flip() +
  facet_grid(.~performed_by) +
  labs(x="", y="", fill="Performed by",
       title = "GROSS DOMESTIC EXPENDITURE ON R&D (OECD MEMBER COUNTRIES)",
       subtitle = "Percentage performed by Government, Higher education and the Business enterprise sector (2015)",
       caption = "Source: OECD, Main Science and Technology Indicators, 2018. | DataViz: Lorna Campos Matus") +
  theme_hc() +
  geom_text(aes(label=percentage_performed_by), color="black", family="Arial Rounded MT Bold", size=3, vjust=.38, hjust=-.3)+
  theme(strip.background = element_blank(),
        strip.text.x = element_blank(), 
        axis.text.x = element_blank(),
        axis.text=element_text(size=10, color = "black"),
        title=element_text(color="black",size=10, family="A Love of Thunder")) +
  scale_fill_manual(values=c("#c7093f", "#9bb401", "#008b8e")) + 
  scale_color_manual(values=c("#c7093f"="Government", "#9bb401"="Higher education", "#008b8e"="Industry"))

knitr::include_graphics("data/Barplot - Performed.png")

Designing an interactive choropleth map

Let’s start by getting global data and geometries from Natural Earth

Map.stat <- st_as_sf(countries50) %>% 
  select(name, geometry)

Joining and filtering data with geometries

Map.stat1 <-left_join(Map.stat, researchers, by=c("name"="country")) %>% 
  filter(name!="Antarctica", !is.na(total_researchers_in_full_time)) %>% 
  select(name, total_researchers_in_full_time, geometry)

head(Map.stat1)

## Simple feature collection with 6 features and 2 fields
## geometry type:  MULTIPOLYGON
## dimension:      XY
## bbox:           xmin: -109.4341 ymin: -55.8917 xmax: 18.83223 ymax: 55.05874
## epsg (SRID):    4326
## proj4string:    +proj=longlat +datum=WGS84 +no_defs
##          name total_researchers_in_full_time
## 1     Austria                          43562
## 2     Belgium                          53178
## 3 Switzerland                          43740
## 4       Chile                           8175
## 5  Czech Rep.                          38081
## 6     Germany                         387982
##                         geometry
## 1 MULTIPOLYGON (((16.95312 48...
## 2 MULTIPOLYGON (((4.816016 51...
## 3 MULTIPOLYGON (((9.524023 47...
## 4 MULTIPOLYGON (((-67.5752 -5...
## 5 MULTIPOLYGON (((14.80938 50...
## 6 MULTIPOLYGON (((14.19824 53...

Creating the sequential palette

palette <- colorNumeric(c("#f8d6ea", "#dd86e3", "#ca22b9", "#d53091", "#d5047f", "#a50288"), 
                        domain=Map.stat1$total_researchers_in_full_time)

Setting up the pop up text

popup_sb <- paste0("<b>", Map.stat1$name,"</b><br/>",
                   as.character(round(Map.stat1$total_researchers_in_full_time, 1))) %>% 
  lapply(htmltools::HTML)

Now, let’s go to the map!

leaflet(Map.stat1) %>%
  addTiles() %>%
  setView(25.670689, 35.286792, zoom = 2) %>%
  addPolygons(data = Map.stat1, 
              fillColor = ~palette(total_researchers_in_full_time), 
              fillOpacity = .8, 
              weight = .8, 
              smoothFactor = .2,
              highlight = highlightOptions(
                weight = 3,
                color = "ghostwhite",
                fillOpacity = .9,
                bringToFront = TRUE),
              label=popup_sb,
              labelOptions = labelOptions(
                style = list("font-weight" = "normal", padding = "3px 8px"),
                textsize = "13px",
                direction = "auto")) %>%
  addLegend(pal = palette, values = ~total_researchers_in_full_time, 
            position = "bottomright", 
            title = "Total researchers<br />in full time equivalent")

No data recorded for Australia, Canada, Israel and Mexico

What does the data tell us?

Mexico registers the highest percentage of government financing, surpassing by 17.2 percent points to second-place, Greece.
Japan and Korea register the highest percentage financed by business enterprise sector, reaching 78 % and 74.5 %, respectively.
Mexico leads the percentage performed R&D tasks by government (37.9 %); Latvia, by higher education (49.7 %), and Israel, by the business enterprise sector (85.1 %).
Precisely, this sector is the one that reach first place in the percentage performed R&D tasks in OECD member countries.
And what about the map? Most full-time researchers are located in U.S. (1.379.977), Japan (662.071), Germany (387.982), and Korea (356.447). While, on the opposite side, there’s Iceland (1.944), Luxembourg (2.539), Latvia (3.613), and Estonia (4.187).

Gross Domestic Expenditure on Research and Development

Lorna Campos Matus

2018-09-03